recipe bioconductor-visse

Visualising Set Enrichment Analysis Results






This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

package bioconductor-visse

(downloads) docker_bioconductor-visse



depends bioconductor-gseabase:


depends bioconductor-msigdb:


depends r-base:


depends r-ggforce:

depends r-ggplot2:

depends r-ggraph:

depends r-ggrepel:

depends r-ggwordcloud:

depends r-igraph:

depends r-plyr:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-scales:

depends r-scico:

depends r-textstem:

depends r-tidygraph:

depends r-tm:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-visse

and update with::

   mamba update bioconductor-visse

To create a new environment, run:

mamba create --name myenvname bioconductor-visse

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-visse/tags`_ for valid values for ``<tag>``)

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